#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle.fluid as fluid
import parl
from parl import layers # 封装了paddle.fluid.layers的API


class AtariModel(parl.Model): #
    def __init__(self, act_dim, algo='DQN'):
        self.act_dim = act_dim
        
        # 定义4层全连接网络
        self.conv1 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv2 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv3 = layers.conv2d(
            num_filters=64, filter_size=4, stride=1, padding=1, act='relu')
        self.conv4 = layers.conv2d(
            num_filters=64, filter_size=3, stride=1, padding=1, act='relu')

        self.algo = algo
        if algo == 'Dueling':
            # if使用Dueling，各加两层全连接层计算出As和V
            self.fc1_adv = layers.fc(size=512, act='relu')
            self.fc2_adv = layers.fc(size=act_dim)
            self.fc1_val = layers.fc(size=512, act='relu')
            self.fc2_val = layers.fc(size=1)
        else:
            self.fc1 = layers.fc(size=act_dim) # 否则直接调用全连接层输出的结果

    def value(self, obs):
        obs = obs / 255.0
        out = self.conv1(obs)
        out = layers.pool2d(
            input=out, pool_size=2, pool_stride=2, pool_type='max')
        out = self.conv2(out)
        out = layers.pool2d(
            input=out, pool_size=2, pool_stride=2, pool_type='max')
        out = self.conv3(out)
        out = layers.pool2d(
            input=out, pool_size=2, pool_stride=2, pool_type='max')
        out = self.conv4(out)
        out = layers.flatten(out, axis=1)

        if self.algo == 'Dueling':
            As = self.fc2_adv(self.fc1_adv(out)) # 计算As
            V = self.fc2_val(self.fc1_val(out)) # 计算V
            Q = As + (V - layers.reduce_mean(As, dim=1, keep_dim=True)) # 计算Q值
        else:
            Q = self.fc1(out) # 输出Q
        return Q # 返回Q值
